A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data
Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33139/ http://umpir.ump.edu.my/id/eprint/33139/1/A%20review%20on%20recent%20progress%20in%20machine%20learning%20and%20deep%20learning.pdf |
| _version_ | 1848824183949099008 |
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| author | Mazlan, A. U. Sahabudin, N. A. Remli, M. A. Ismail, N. S. N. Mohamad, M. S. Nies, H. W. Warif, N. B. A. |
| author_facet | Mazlan, A. U. Sahabudin, N. A. Remli, M. A. Ismail, N. S. N. Mohamad, M. S. Nies, H. W. Warif, N. B. A. |
| author_sort | Mazlan, A. U. |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications. |
| first_indexed | 2025-11-15T03:08:59Z |
| format | Article |
| id | ump-33139 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:08:59Z |
| publishDate | 2021 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-331392022-09-02T07:04:40Z http://umpir.ump.edu.my/id/eprint/33139/ A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data Mazlan, A. U. Sahabudin, N. A. Remli, M. A. Ismail, N. S. N. Mohamad, M. S. Nies, H. W. Warif, N. B. A. Q Science (General) QA76 Computer software QC Physics Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications. MDPI AG 2021-08 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/33139/1/A%20review%20on%20recent%20progress%20in%20machine%20learning%20and%20deep%20learning.pdf Mazlan, A. U. and Sahabudin, N. A. and Remli, M. A. and Ismail, N. S. N. and Mohamad, M. S. and Nies, H. W. and Warif, N. B. A. (2021) A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data. Processes, 9 (8). pp. 1-12. ISSN 2227-9717. (Published) https://doi.org/ 10.3390/pr9081466 https://doi.org/ 10.3390/pr9081466 |
| spellingShingle | Q Science (General) QA76 Computer software QC Physics Mazlan, A. U. Sahabudin, N. A. Remli, M. A. Ismail, N. S. N. Mohamad, M. S. Nies, H. W. Warif, N. B. A. A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data |
| title | A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data |
| title_full | A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data |
| title_fullStr | A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data |
| title_full_unstemmed | A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data |
| title_short | A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data |
| title_sort | review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data |
| topic | Q Science (General) QA76 Computer software QC Physics |
| url | http://umpir.ump.edu.my/id/eprint/33139/ http://umpir.ump.edu.my/id/eprint/33139/ http://umpir.ump.edu.my/id/eprint/33139/ http://umpir.ump.edu.my/id/eprint/33139/1/A%20review%20on%20recent%20progress%20in%20machine%20learning%20and%20deep%20learning.pdf |